MSNA Analysis Results

0.1 In which region is the interview taking place?

0.1.1 How many households would you estimate there to be in your village?“”

0.2 Graph

0.3 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	60.72165	45.72520	75.71810
dependent.var1	capital_central	36.29179	23.72339	48.86019
dependent.var2	north_east	44.31057	34.11060	54.51053
dependent.var3	west	59.17154	50.59274	67.75033
dependent.var4	east	50.69055	42.76680	58.61431
dependent.var5	south	47.15459	35.52909	58.78010
dependent.var6	north	44.95590	39.77987	50.13193

0.4 Statistics

two sample ttest on difference in means (two sided): p=0.058; df=450

0.4.1 Is the HH in an informal settlement?

0.5 Graph

0.6 Table

informal_settlement	region	numbers	lover conf. limit	upper conf. limit
no	capital_central	0.7654511	0.5284897	1.0000000
no	east	0.8604435	0.7297095	0.9911776
no	north	0.9285158	0.8521533	1.0000000
no	north_east	0.9996894	0.9993380	1.0000000
no	south	0.8364660	0.7111739	0.9617580
no	south_east	0.8616757	0.7176960	1.0000000
no	west	1.0000000	1.0000000	1.0000000
yes	capital_central	0.2345489	0.0000000	0.4715103
yes	east	0.1395565	0.0088224	0.2702905
yes	north	0.0714842	0.0000000	0.1478467
yes	north_east	0.0003106	0.0000000	0.0006620
yes	south	0.1635340	0.0382420	0.2888261
yes	south_east	0.1383243	0.0000000	0.2823040
yes	west	0.0000000	0.0000000	0.0000000

0.7 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1049.2

0.7.1 Do you consider your household to be in which of the following options?:

0.8 Graph

0.9 Table

urban_rural	region	numbers	lover conf. limit	upper conf. limit
rural	capital_central	0.0859723	0.0000000	0.2386351
rural	east	0.6878900	0.5192686	0.8565115
rural	north	0.1841642	0.0000000	0.3699684
rural	north_east	0.1498344	0.0019484	0.2977204
rural	south	0.3054369	0.1479624	0.4629113
rural	south_east	0.8463037	0.7022782	0.9903291
rural	west	0.6537766	0.5186535	0.7888997
urban	capital_central	0.2998078	0.1053762	0.4942395
urban	east	0.0818542	0.0000000	0.1656368
urban	north	0.0278835	0.0147803	0.0409867
urban	north_east	0.3783004	0.1836979	0.5729029
urban	south	0.4961044	0.3281502	0.6640587
urban	south_east	0.0606195	0.0000000	0.1685505
urban	west	0.2670864	0.1516881	0.3824847
peri_urban	capital_central	0.6142199	0.3670587	0.8613811
peri_urban	east	0.2302557	0.0736241	0.3868873
peri_urban	north	0.7879523	0.6019649	0.9739397
peri_urban	north_east	0.4718652	0.2693940	0.6743364
peri_urban	south	0.1984587	0.0792751	0.3176423
peri_urban	south_east	0.0930769	0.0000000	0.2028657
peri_urban	west	0.0791370	0.0000000	0.1652041

0.10 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=2639.1

0.10.1 Is your community densely populated?

0.11 Graph

0.12 Table

densely_populated	region	numbers	lover conf. limit	upper conf. limit
no	capital_central	0.0049895	0.0004028	0.0095761
no	east	0.2115835	0.0703957	0.3527714
no	north	0.5785194	0.3422508	0.8147880
no	north_east	0.1218779	0.0000000	0.2527405
no	south	0.2238099	0.0872464	0.3603734
no	south_east	0.6757398	0.4732629	0.8782166
no	west	0.2855492	0.1327553	0.4383431
yes	capital_central	0.9950105	0.9904239	0.9995972
yes	east	0.7884165	0.6472286	0.9296043
yes	north	0.4214806	0.1852120	0.6577492
yes	north_east	0.8781221	0.7472595	1.0000000
yes	south	0.7761901	0.6396266	0.9127536
yes	south_east	0.3242602	0.1217834	0.5267371
yes	west	0.7144508	0.5616569	0.8672447

0.13 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1319.6

0.13.1 Do you have physical access to a market?

0.14 Graph

0.15 Table

access_to_market	region	numbers	lover conf. limit	upper conf. limit
no	capital_central	0.1476270	0.0000000	0.3251322
no	east	0.1703880	0.0406819	0.3000942
no	north	0.1401449	0.0000000	0.3132652
no	north_east	0.0709255	0.0000000	0.1741371
no	south	0.1309661	0.0622704	0.1996618
no	south_east	0.3163729	0.1145840	0.5181617
no	west	0.2369566	0.0961317	0.3777814
yes	capital_central	0.8523730	0.6748678	1.0000000
yes	east	0.8296120	0.6999058	0.9593181
yes	north	0.8598551	0.6867348	1.0000000
yes	north_east	0.9290745	0.8258629	1.0000000
yes	south	0.8690339	0.8003382	0.9377296
yes	south_east	0.6836271	0.4818383	0.8854160
yes	west	0.7630434	0.6222186	0.9038683

0.16 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1520.5

0.16.1 Do you agree to be contacted for any follow up questions regarding this assessment?

0.17 Graph

0.18 Table

agree_to_be_contacted	region	numbers	lover conf. limit	upper conf. limit
no	capital_central	0.3626020	0.1154985	0.6097054
no	east	0.0329167	0.0000000	0.0874895
no	north	0.9067940	0.8310268	0.9825612
no	north_east	0.4477299	0.2758766	0.6195833
no	south	0.4623950	0.2962485	0.6285416
no	south_east	0.9403237	0.8324112	1.0000000
no	west	0.2376247	0.1221032	0.3531462
yes	capital_central	0.6373980	0.3902946	0.8845015
yes	east	0.9670833	0.9125105	1.0000000
yes	north	0.0932060	0.0174388	0.1689732
yes	north_east	0.5522701	0.3804167	0.7241234
yes	south	0.5376050	0.3714584	0.7037515
yes	south_east	0.0596763	0.0000000	0.1675888
yes	west	0.7623753	0.6468538	0.8778968

0.19 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1410.4

0.19.1 Is the household head male or female?

0.20 Graph

0.21 Table

head_hh_gendre	region	numbers	lover conf. limit	upper conf. limit
male	capital_central	0.9932326	0.9873176	0.9991476
male	east	0.8845817	0.7626671	1.0000000
male	north	0.9963517	0.9904945	1.0000000
male	north_east	0.8917533	0.7619665	1.0000000
male	south	0.9517157	0.9028104	1.0000000
male	south_east	0.9391088	0.8306868	1.0000000
male	west	0.9089502	0.8205162	0.9973841
female	capital_central	0.0067674	0.0008524	0.0126824
female	east	0.1154183	0.0000000	0.2373329
female	north	0.0036483	0.0000000	0.0095055
female	north_east	0.1082467	0.0000000	0.2380335
female	south	0.0482843	0.0000000	0.0971896
female	south_east	0.0608912	0.0000000	0.1693132
female	west	0.0910498	0.0026159	0.1794838

0.22 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=960.1

0.22.1 How old is the household head?

0.23 Graph

0.24 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	48.92053	45.51463	52.32643
dependent.var1	capital_central	43.09471	39.32370	46.86572
dependent.var2	north_east	45.07540	40.37499	49.77580
dependent.var3	west	47.28778	42.81546	51.76010
dependent.var4	east	43.78893	40.45028	47.12757
dependent.var5	south	45.79618	41.78259	49.80977
dependent.var6	north	47.04796	40.63152	53.46441

0.25 Statistics

two sample ttest on difference in means (two sided): p=0.787; df=450

0.25.1 Does the household head have a physical or mental disability that affects daily life?

0.26 Graph

0.27 Table

head_hh_disability	region	numbers	lover conf. limit	upper conf. limit
no	capital_central	0.9260707	0.7886016	1.0000000
no	east	0.9585870	0.8777366	1.0000000
no	north	0.9879302	0.9776471	0.9982134
no	north_east	0.8525609	0.6930337	1.0000000
no	south	0.9810805	0.9589760	1.0000000
no	south_east	0.8352101	0.6736308	0.9967895
no	west	0.7324741	0.5819528	0.8829953
yes	capital_central	0.0739293	0.0000000	0.2113984
yes	east	0.0414130	0.0000000	0.1222634
yes	north	0.0120698	0.0017866	0.0223529
yes	north_east	0.1474391	0.0000000	0.3069663
yes	south	0.0189195	0.0000000	0.0410240
yes	south_east	0.1647899	0.0032105	0.3263692
yes	west	0.2675259	0.1170047	0.4180472

0.28 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1322.9

0.28.1 How many individuals are in the household?

0.29 Graph

0.30 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	12.99550	8.588316	17.40269
dependent.var1	capital_central	17.03438	11.380373	22.68838
dependent.var2	north_east	14.21727	10.843482	17.59106
dependent.var3	west	20.58123	18.698212	22.46425
dependent.var4	east	13.89538	10.497414	17.29334
dependent.var5	south	17.67935	14.975385	20.38332
dependent.var6	north	11.34279	7.427003	15.25858

0.31 Statistics

two sample ttest on difference in means (two sided): p=0.351; df=450

0.31.1 Number of Female New born ( < 2yr)

0.32 Graph

0.33 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	1.1955399	0.7618014	1.6292784
dependent.var1	capital_central	0.3120975	0.0497760	0.5744191
dependent.var2	north_east	0.5281456	0.2519025	0.8043888
dependent.var3	west	0.4206420	0.2694100	0.5718740
dependent.var4	east	0.4413480	0.2397642	0.6429317
dependent.var5	south	0.5509862	0.3501815	0.7517908
dependent.var6	north	0.7182147	0.3288202	1.1076091

0.34 Statistics

two sample ttest on difference in means (two sided): p=0.444; df=450

0.34.1 If Number of Female New born “”( < 2yr) > 0“”, how many are physically disabled?

0.35 Graph

0.36 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0102298	-0.0121868	0.0326463
dependent.var1	south_east	0.0000000	0.0000000	0.0000000
dependent.var2	south	0.0324698	-0.0311698	0.0961095
dependent.var3	north	0.1911712	-0.0548270	0.4371694
dependent.var4	west	0.0000000	0.0000000	0.0000000
dependent.var5	east	0.0015534	-0.0017817	0.0048885
dependent.var6	capital_central	0.0106608	0.0106608	0.0106608

0.37 Statistics

two sample ttest on difference in means (two sided): p=0; df=156

0.37.1 If Number of Female New born ( < 2yr) > 0, how many have mental health concerns?

0.38 Graph

0.39 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0000000	0.0000000	0.0000000
dependent.var1	south_east	0.0045385	-0.0018263	0.0109033
dependent.var2	south	0.0000000	0.0000000	0.0000000
dependent.var3	north	0.0000000	0.0000000	0.0000000
dependent.var4	west	0.0000000	0.0000000	0.0000000
dependent.var5	east	0.0000000	0.0000000	0.0000000
dependent.var6	capital_central	0.0000000	0.0000000	0.0000000

0.40 Statistics

two sample ttest on difference in means (two sided): p=0.353; df=156

0.40.1 If Number of Female New born ( < 2yr) > 0, how many are chronically ill?

0.41 Graph

0.42 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0102298	-0.0121868	0.0326463
dependent.var1	south_east	0.0278207	-0.0034949	0.0591362
dependent.var2	south	0.0000000	0.0000000	0.0000000
dependent.var3	north	0.0077342	-0.0033309	0.0187994
dependent.var4	west	0.0000000	0.0000000	0.0000000
dependent.var5	east	0.0000000	0.0000000	0.0000000
dependent.var6	capital_central	0.0004523	-0.0005277	0.0014323

0.43 Statistics

two sample ttest on difference in means (two sided): p=0.34; df=156

0.43.1 Number of Male New born ( < 2yr)

0.44 Graph

0.45 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	1.2072718	0.5978235	1.8167201
dependent.var1	capital_central	0.2305190	-0.0035799	0.4646179
dependent.var2	north_east	0.3243507	0.1024034	0.5462981
dependent.var3	west	0.2720817	0.1268115	0.4173519
dependent.var4	east	0.3718329	0.1991693	0.5444965
dependent.var5	south	0.6048204	0.3373487	0.8722920
dependent.var6	north	0.6152756	0.2785460	0.9520052

0.46 Statistics

two sample ttest on difference in means (two sided): p=0.342; df=450

0.46.1 If Number of Male New born ( < 2 yr) > 0, how many are physically disabled?

0.47 Graph

0.48 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0000000	0.0000000	0.0000000
dependent.var1	capital_central	0.0100063	-0.0116756	0.0316883
dependent.var2	south	0.0000000	0.0000000	0.0000000
dependent.var3	south_east	0.0000000	0.0000000	0.0000000
dependent.var4	east	0.0000000	0.0000000	0.0000000
dependent.var5	west	0.0000000	0.0000000	0.0000000
dependent.var6	north	0.3510706	-0.0069104	0.7090516

0.49 Statistics

two sample ttest on difference in means (two sided): p=0.351; df=128

0.49.1 If Number of Male New born ( < 2 yr) > 0, how many have mental health concerns?

0.50 Graph

0.51 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0000000	0.0000000	0.0000000
dependent.var1	capital_central	0.0002116	0.0002116	0.0002116
dependent.var2	south	0.0000000	0.0000000	0.0000000
dependent.var3	south_east	0.0518052	0.0264211	0.0771894
dependent.var4	east	0.0000000	0.0000000	0.0000000
dependent.var5	west	0.0000000	0.0000000	0.0000000
dependent.var6	north	0.0702141	-0.0784294	0.2188576

0.52 Statistics

two sample ttest on difference in means (two sided): p=0; df=128

0.52.1 If Number of Male New born ( < 2 yr ) > 0, how many are chronically ill?

0.53 Graph

0.54 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	north_east	0.0000000	0.0000000	0.0000000
dependent.var1	capital_central	0.0000000	0.0000000	0.0000000
dependent.var2	south	0.0000000	0.0000000	0.0000000
dependent.var3	south_east	0.0061526	-0.0023922	0.0146974
dependent.var4	east	0.0000000	0.0000000	0.0000000
dependent.var5	west	0.0886989	-0.0851477	0.2625454
dependent.var6	north	0.0000000	0.0000000	0.0000000

0.55 Statistics

two sample ttest on difference in means (two sided): p=0.341; df=128

0.55.1 Number of young girls (2< 5yr)

0.56 Graph

0.57 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	south_east	0.7320491	0.2837206	1.1803776
dependent.var1	capital_central	0.5406082	0.2581904	0.8230261
dependent.var2	north_east	0.5724631	0.2081867	0.9367395
dependent.var3	west	0.4101118	0.1798578	0.6403657
dependent.var4	east	0.6781030	0.3996715	0.9565344
dependent.var5	south	0.5705999	0.3229213	0.8182784
dependent.var6	north	1.0802127	0.6560990	1.5043264

0.58 Statistics

two sample ttest on difference in means (two sided): p=0.497; df=450

0.58.1 If >0, how many are physically disabled?

0.59 Graph

0.60 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	capital_central	0.0007964	-0.0007646	0.0023575
dependent.var1	north_east	0.0000000	0.0000000	0.0000000
dependent.var2	east	0.0000000	0.0000000	0.0000000
dependent.var3	south	0.0000000	0.0000000	0.0000000
dependent.var4	south_east	0.0163084	-0.0027222	0.0353389
dependent.var5	west	0.0000000	0.0000000	0.0000000
dependent.var6	north	0.0526606	-0.0588220	0.1641432

0.61 Statistics

two sample ttest on difference in means (two sided): p=0.334; df=202

0.61.1 If >0, how many have mental health concerns?

0.62 Graph

0.63 Table

	region	numbers	lover conf. limit	upper conf. limit
dependent.var	capital_central	0.0001286	-0.0001235	0.0003807
dependent.var1	north_east	0.0000000	0.0000000	0.0000000
dependent.var2	east	0.0000000	0.0000000	0.0000000
dependent.var3	south	0.0000000	0.0000000	0.0000000
dependent.var4	south_east	0.0000000	0.0000000	0.0000000
dependent.var5	west	0.0000000	0.0000000	0.0000000
dependent.var6	north	0.0000000	0.0000000	0.0000000

0.64 Statistics